The rapid proliferation of Unmanned Aerial Vehicles (UAVs) has opened up new horizons in various application domains. However, the optimal selection of UAV services remains a complex challenge, necessitating a nuanced approach considering multiple criteria. This article proposes a novel framework that integrates machine learning (ML) techniques with multi-criteria decision-making (MCDM) methods to address this challenge. Our framework comprehensively evaluates UAV services based on key metrics such as delay, packet loss, throughput, and residual energy, thereby providing a holistic perspective for decision-making. We present experimental results from a comparative study involving Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. Our findings reveal the effectiveness of our proposed approach, which achieved a classification accuracy of 99.9% with RF. These results underscore the capability of our framework to optimize UAV service delivery, thus enhancing overall performance and user satisfaction. By coupling ML and MCDM, our framework offers a robust and efficient solution for UAV service selection, catering to the diverse needs of users and service providers in the evolving landscape of UAV applications.
MCDM: an ML-based Multi-Criteria Decision Making solution for UAVs Service Selection
24.04.2024
1111489 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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